Evidence map›Paper›PMID 37856021›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

A Novel Sandwich Method for Serial Block Face SEM Imaging of Airway Multiciliated Epithelium.

Nobuhiro Morone, Maria Guerra Martin, Patricia Goggin, Kirk J Czymmek, Vito Mennella, Jaime Llodra Gonzalez

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Nobuhiro MoroneElectron Microscopy and Ultrastructural Pathology Facility, Medical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK. nm669@mrc-tox.cam.ac.uk.
Maria Guerra MartinElectron Microscopy and Ultrastructural Pathology Facility, Medical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK.
Patricia GogginBiomedical Imaging Facility, Laboratory and Pathology Block, Southampton General Hospital, Southampton, UK.
Kirk J CzymmekDonald Danforth Plant Science Center, Department of Biology, Saint Louis University, Saint Louis, MO, USA.
Vito MennellaMedical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK.
Jaime Llodra GonzalezElectron Microscopy and Ultrastructural Pathology Facility, Medical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK.
Medical Research Council · GBUniversity of Cambridge · GBSaint Louis University · USSouthampton General Hospital · GB

Funding

Medical Research Council MC_UU_00025/13
6 · The paper itself

Abstract

Volume electron microscopy technologies such as serial block face scanning electron microscopy (SBF-SEM) allow the characterization of tissue organization and cellular content in three dimensions at nanoscale resolution. Here, we describe the procedure to process and image an air-liquid interface culture of human or mouse airway epithelial cells for visualization of the multiciliated epithelium by SBF-SEM in vertical or horizontal cross section.

Indexed as

Imaging, Three-DimensionalVolume Electron MicroscopyAnimalsEpithelial CellsEpitheliumHumansMiceMicroscopy, Electron, ScanningAir-liquid interface culturesAirway epithelial cellsSandwich embeddingSerial block face scanning electron microscopyVolumeEM

Identifiers

PMID37856021
OpenAlexW4387765588

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.